BLAS Level 3 Routines

BLAS Level 3 routines perform matrix-matrix operations. The following table lists the BLAS Level 3 routine groups and the data types associated with them.

BLAS Level 3 Routine Groups and Their Data Types
Routine Group	Data Types	Description
cblas_?gemm	s, d, c, z	Computes a matrix-matrix product with general matrices.
cblas_?hemm	c, z	Computes a matrix-matrix product where one input matrix is Hermitian.
cblas_?herk	c, z	Performs a Hermitian rank-k update.
cblas_?her2k	c, z	Performs a Hermitian rank-2k update.
cblas_?symm	s, d, c, z	Computes a matrix-matrix product where one input matrix is symmetric.
cblas_?syrk	s, d, c, z	Performs a symmetric rank-k update.
cblas_?syr2k	s, d, c, z	Performs a symmetric rank-2k update.
cblas_?trmm	s, d, c, z	Computes a matrix-matrix product where one input matrix is triangular.
cblas_?trsm	s, d, c, z	Solves a triangular matrix equation.

Symmetric Multiprocessing Version of Intel® MKL

Many applications spend considerable time executing BLAS routines. This time can be scaled by the number of processors available on the system through using the symmetric multiprocessing (SMP) feature built into the Intel® oneMKL. The performance enhancements based on the parallel use of the processors are available without any programming effort on your part.

To enhance performance, the library uses the following methods:

The BLAS functions are blocked where possible to restructure the code in a way that increases the localization of data reference, enhances cache memory use, and reduces the dependency on the memory bus.
The code is distributed across the processors to maximize parallelism.

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex. Notice revision #20201201

Product and Performance Information

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.

Notice revision #20201201